AWS Big Data Blog

Combine Transactional and Analytical Data Using Amazon Aurora and Amazon Redshift

A few months ago, we published a blog post about capturing data changes in an Amazon Aurora database and sending it to Amazon Athena and Amazon QuickSight for fast analysis and visualization. In this post, I want to demonstrate how easy it can be to take the data in Aurora and combine it with data in Amazon Redshift using Amazon Redshift Spectrum.

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Now Available: New Digital Training to Help You Learn About AWS Big Data Services

by Sara Snedeker | on | Permalink | Comments |  Share

AWS Training and Certification recently released free digital training courses that will make it easier for you to build your cloud skills and learn about using AWS Big Data services. This training includes courses like Introduction to Amazon EMR and Introduction to Amazon Athena.

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Power data ingestion into Splunk using Amazon Kinesis Data Firehose

With Kinesis Data Firehose, customers can use a fully managed, reliable, and scalable data streaming solution to Splunk. In this post, we tell you a bit more about the Kinesis Data Firehose and Splunk integration. We also show you how to ingest large amounts of data into Splunk using Kinesis Data Firehose.

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Simplify Querying Nested JSON with the AWS Glue Relationalize Transform

AWS Glue has a transform called Relationalize that simplifies the extract, transform, load (ETL) process by converting nested JSON into columns that you can easily import into relational databases. Relationalize transforms the nested JSON into key-value pairs at the outermost level of the JSON document. The transformed data maintains a list of the original keys from the nested JSON separated by periods. Let’s look at how Relationalize can help you with a sample use case.

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Genomic Analysis with Hail on Amazon EMR and Amazon Athena

For this task, we use Hail, an open source framework for exploring and analyzing genomic data that uses the Apache Spark framework. In this post, we use Amazon EMR to run Hail. We walk through the setup, configuration, and data processing. Finally, we generate an Apache Parquet–formatted variant dataset and explore it using Amazon Athena.

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Collect Data Statistics Up to 5x Faster by Analyzing Only Predicate Columns with Amazon Redshift

After loading new data into an Amazon Redshift cluster, statistics need to be re-computed to guarantee performant query plans. By learning which column statistics are actually being used by the customer’s workload and collecting statistics only on those columns, Amazon Redshift is able to significantly reduce the amount of time needed for table maintenance during data loading workflows.

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Using Amazon Redshift Spectrum, Amazon Athena, and AWS Glue with Node.js in Production

This is a guest post by Rafi Ton, founder and CEO of NUVIAD. The ability to provide fresh, up-to-the-minute data to our customers and partners was always a main goal with our platform. We saw other solutions provide data that was a few hours old, but this was not good enough for us. We insisted on providing the freshest data possible. For us, that meant loading Amazon Redshift in frequent micro batches and allowing our customers to query Amazon Redshift directly to get results in near real time. The benefits were immediately evident. Our customers could see how their campaigns performed faster than with other solutions, and react sooner to the ever-changing media supply pricing and availability. They were very happy.

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AWS Big Data & Analytics Sessions at Re:Invent 2017

We can’t believe that there are just few days left before re:Invent 2017. If you are attending this year, you’ll want to check out our Big Data sessions! The Big Data and Machine Learning categories are bigger than ever. This post highlights the sessions that will be presented as part of the Analytics & Big Data track, as well as relevant sessions from other tracks like Architecture, Artificial Intelligence & Machine Learning, and IoT.

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Create an Amazon Redshift Data Warehouse That Can Be Securely Accessed Across Accounts

Data security is paramount in many industries. Organizations that shift their IT infrastructure to the cloud must ensure that their data is protected and that the attack surface is minimized. This post focuses on a method of securely loading a subset of data from one Amazon Redshift cluster to another Amazon Redshift cluster that is located in a different AWS account.

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